A P53-Deficiency Gene Signature Predicts Recurrence Risk of Patients with Early-Stage Lung Adenocarcinoma

Zhao, Yanding; Varn, Frederick S.; Cai, Guoshuai; Xiao, Feifei; Amos, Christopher I.; Cheng, Chao

doi:10.1158/1055-9965.epi-17-0478

Cited by 44 publications

(46 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This change occurs because the microenvironment of cancer tissue lesions is usually accompanied by the local inflammatory response, which activates the body’s stress response and immune response. In the initial stage, under the regulation of innate immunity and adaptive immunity, multiple genes are differentially expressed to compensate for function and resist cancer [ 26 ]. However, the low sensitivity and specificity impede the broad application of these biomarkers in clinics.…”

Section: Discussionmentioning

confidence: 99%

Identification of an early diagnostic biomarker of lung adenocarcinoma based on co-expression similarity and construction of a diagnostic model

Fan

Xue

et al. 2018

J Transl Med

View full text Add to dashboard Cite

BackgroundThe purpose of this study was to achieve early and accurate diagnosis of lung cancer and long-term monitoring of the therapeutic response.MethodsWe downloaded GSE20189 from GEO database as analysis data. We also downloaded human lung adenocarcinoma RNA-seq transcriptome expression data from the TCGA database as validation data. Finally, the expression of all of the genes underwent z test normalization. We used ANOVA to identify differentially expressed genes specific to each stage, as well as the intersection between them. Two methods, correlation analysis and co-expression network analysis, were used to compare the expression patterns and topological properties of each stage. Using the functional quantification algorithm, we evaluated the functional level of each significantly enriched biological function under different stages. A machine-learning algorithm was used to screen out significant functions as features and to establish an early diagnosis model. Finally, survival analysis was used to verify the correlation between the outcome and the biomarkers that we found.ResultsWe screened 12 significant biomarkers that could distinguish lung cancer patients with diverse risks. Patients carrying variations in these 12 genes also presented a poor outcome in terms of survival status compared with patients without variations.ConclusionsWe propose a new molecular-based noninvasive detection method. According to the expression of the stage-specific gene set in the peripheral blood of patients with lung cancer, the difference in the functional level is quantified to realize the early diagnosis and prediction of lung cancer.

show abstract

Section: Discussionmentioning

confidence: 99%

Identification of an early diagnostic biomarker of lung adenocarcinoma based on co-expression similarity and construction of a diagnostic model

Fan

Xue

et al. 2018

J Transl Med

View full text Add to dashboard Cite

show abstract

“…Given the expression profiles for a number of breast cancer patients, sample-specific RPSs were calculated for all samples based on the RPS gene signature. Specifically, a modified version of a statistical method called BASE [38][39][40][41][42] was applied as follows: first, gene expression profiles were median normalized to relative gene expression for each gene across samples. Second, for each sample, its gene expression profile was sorted in a descending order based on the relative expression to obtain an expression profile (e 1 , e 2 , …, e g ), where g was the total number of genes.…”

Section: Calculation Of Rps and Tnbc-rps In Pretreated Breast Cancementioning

confidence: 99%

Gene signature‐based prediction of triple‐negative breast cancer patient response to Neoadjuvant chemotherapy

2020

Self Cite

View full text Add to dashboard Cite

Neoadjuvant chemotherapy is the current standard of care for large, advanced, and/or inoperable tumors, including triple‐negative breast cancer. Although the clinical benefits of neoadjuvant chemotherapy have been illustrated through numerous clinical trials, more than half of the patients do not experience therapeutic benefit and needlessly suffer from side effects. Currently, no clinically applicable biomarkers are available for predicting neoadjuvant chemotherapy response in triple‐negative breast cancer; the discovery of such a predictive biomarker or marker profile is an unmet need. In this study, we introduce a generic computational framework to calculate a response‐probability score (RPS), based on patient transcriptomic profiles, to predict their response to neoadjuvant chemotherapy. We first validated this framework in ER‐positive breast cancer patients and showed that it predicted neoadjuvant chemotherapy response with equal performance to several clinically used gene signatures, including Oncotype DX and MammaPrint. Then, we applied this framework to triple‐negative breast cancer data and, for each patient, we calculated a response probability score (TNBC‐RPS). Our results indicate that the TNBC‐RPS achieved the highest accuracy for predicting neoadjuvant chemotherapy response compared to previously proposed 143 gene signatures. When combined with additional clinical factors, the TNBC‐RPS achieved a high prediction accuracy for triple‐negative breast cancer patients, which was comparable to the prediction accuracy of Oncotype DX and MammaPrint in ER‐positive patients. In conclusion, the TNBC‐RPS accurately predicts neoadjuvant chemotherapy response in triple‐negative breast cancer patients and has the potential to be clinically used to aid physicians in stratifying patients for more effective neoadjuvant chemotherapy.

show abstract

“…It is vital to understand the molecular mechanisms of cancer recurrence. [6][7][8][9] Many genes have been found to regulate cancer recurrence.…”

Section: Introductionmentioning

confidence: 99%

Bioinformatics analysis suggests that COL4A1 may play an important role in gastric carcinoma recurrence

Wang

Chang

et al. 2019

J of Digest Diseases

View full text Add to dashboard Cite

Objective Cancer recurrence is a complicated problem for clinicians that contributes to poor prognosis. This study aimed to use advanced gastric carcinoma genes profiles to predict increased risk of cancer recurrence in order to identify patients in need of adjuvant therapy for prognosis improvement. Methods Differentially expressed genes were identified for advanced gastric carcinoma by analyzing the GSE2685 from the Gene Expression Omnibus database (GEO) using R package. The candidate genes were then obtained by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, protein‐protein interaction analysis and survival analysis. Logistic regression analysis was performed to determine the relationship between candidate genes and the recurrence of gastric carcinoma. Results Collagen type IV alpha 1 (COL4A1) was overexpressed in gastric carcinoma tissue by analyzing the GSE2685 gene expression profiles from the Gene Expression Omnibus database. COL4A1 was also overexpressed in gastric carcinoma tissue from the Cancer Genome Atlas dataset and further determined that higher COL4A1 expression led to poorer overall survival. A univariate analysis suggested that COL4A1 was strongly correlated with T stage and gastric carcinoma recurrence (P = 0.014 and 0.041, respectively). Moreover, a multiple logistic regression analysis indicated that COL4A1 was significantly associated with gastric carcinoma recurrence (hazard ratio 1.605, 95% confidence interval 1.063‐2.677, P = 0.008). Conclusions COL4A1 may promote gastric carcinoma recurrence and could be used as a therapeutic target for gastric carcinoma recurrence.

show abstract

A P53-Deficiency Gene Signature Predicts Recurrence Risk of Patients with Early-Stage Lung Adenocarcinoma

Cited by 44 publications

References 50 publications

Identification of an early diagnostic biomarker of lung adenocarcinoma based on co-expression similarity and construction of a diagnostic model

Identification of an early diagnostic biomarker of lung adenocarcinoma based on co-expression similarity and construction of a diagnostic model

Gene signature‐based prediction of triple‐negative breast cancer patient response to Neoadjuvant chemotherapy

Bioinformatics analysis suggests that COL4A1 may play an important role in gastric carcinoma recurrence

Contact Info

Product

Resources

About